The present invention is related to ventilators, and more particularly to systems and methods for identification of time dependent signals and/or respiratory parameters in a dynamic ventilation system.
Ventilators are designed to ventilate a patient's lungs with gas, and to thereby assist the patient when the patients ability to breathe on their own is somehow impaired. Ventilation is achieved by providing a defined gas mixture to the patient according to a prescribed ventilation modality. As each patient may require a different ventilation strategy, modern ventilators can be customized for the particular needs of an individual patient.
Modern ventilators are dynamic systems whose dynamic behavior and outputs, such as pressures and flows delivered to the patient, are driven by input signals, such as gas flows. Proper operation of such ventilators relies on some understanding of a variety of respiratory parameters including the resistance of the patient airways and the compliance of the lung. These parameters may vary significantly from one ventilation system to another, and from one patient to another. In many cases, proper operation of a ventilation system is limited by the accuracy at which such parameters are defined or estimated.
Methods for identifying the ventilation parameters for a particular individual or a particular ventilation situation have been developed. Such methods can be divided into two different categories: static methods and dynamic methods. In static methods, respiratory parameters are typically estimated during short periods of induced equilibrium states (i.e., maneuvers) of the system using only a few measurements of quantities that are related to the estimated parameters. In contrast, dynamic methods operate to describe the dynamic behavior of the patient under ventilation, and are typically based on continuous or segmented continuous measurement of ventilator conditions. Historically, identifying respiratory parameters posed a challenge in the case of the ventilation system driven by unknown input signals. This is the case with the ventilation systems involving actively breathing patients and leaks, and many existing approaches fail to provide sufficiently accurate results because these signals driving the system typically cannot be measured but they must be accounted for in the identification algorithms. For example, various approaches for estimating patient breathing effort are inaccurate, and as such dynamic methods relying on an estimated patient effort are often inadequate.
In some cases, patient breathing effort has been estimated using the equation of motion, and relying exclusively on the measurement of gas flow in and out of the patient's lungs along with a pressure measurement. The reliability of such an approach is limited by the accuracy at which gas flow in and out of the patient's lungs may be measured. Such a measurement, however, is inherently inaccurate as it relies on a flow sensor at or near a tube inserted in the patient's trachea. The accuracy of the flow sensor is substantially reduced due to the humidity of gas exhaled from the lung. Further, such a flow sensor near the patient's trachea is often not available in existing ventilation systems.
Hence, there exists a need in the art for advanced ventilation systems, and methods for using such.